The early identification of Amyotrophic Lateral Sclerosis (ALS), a progressive neurological disease, using blood-based transcriptome biomarker is gaining attention. The classification of ALS from blood transcriptomic data remains challenging due to class imbalance and high dimensionality. This extension of a previous study that utilized machine learning on the microarray dataset includes a synthetic data augmentation method employing the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. Following the use of Fisher Score, t-test, PCA, and Ant Colony Optimization for feature selection, SMOTE was employed to produce synthetic ALS samples and to imbalance the... More >